# naginterfaces.library.lapackeig.dgelsd¶

naginterfaces.library.lapackeig.dgelsd(a, b, rcond)[source]

dgelsd computes the minimum norm solution to a real linear least squares problem

For full information please refer to the NAG Library document for f08kc

https://www.nag.com/numeric/nl/nagdoc_28.5/flhtml/f08/f08kcf.html

Parameters
afloat, array-like, shape

The coefficient matrix .

bfloat, array-like, shape

The right-hand side matrix .

rcondfloat

Used to determine the effective rank of . Singular values are treated as zero. If , machine precision is used instead.

Returns
afloat, ndarray, shape

The contents of are destroyed.

bfloat, ndarray, shape

is overwritten by the solution matrix . If and , the residual sum of squares for the solution in the th column is given by the sum of squares of elements in that column.

sfloat, ndarray, shape

The singular values of in decreasing order.

rankint

The effective rank of , i.e., the number of singular values which are greater than .

Raises
NagValueError
(errno )

On entry, error in parameter .

Constraint: .

(errno )

On entry, error in parameter .

Constraint: .

(errno )

On entry, error in parameter .

Constraint: .

(errno )

The algorithm for computing the SVD failed to converge; off-diagonal elements of an intermediate bidiagonal form did not converge to zero.

Notes

dgelsd uses the singular value decomposition (SVD) of , where is a real matrix which may be rank-deficient.

Several right-hand side vectors and solution vectors can be handled in a single call; they are stored as the columns of the right-hand side matrix and the solution matrix .

The problem is solved in three steps:

1. reduce the coefficient matrix to bidiagonal form with Householder transformations, reducing the original problem into a ‘bidiagonal least squares problem’ (BLS);

2. solve the BLS using a divide-and-conquer approach;

3. apply back all the Householder transformations to solve the original least squares problem.

The effective rank of is determined by treating as zero those singular values which are less than times the largest singular value.

References

Anderson, E, Bai, Z, Bischof, C, Blackford, S, Demmel, J, Dongarra, J J, Du Croz, J J, Greenbaum, A, Hammarling, S, McKenney, A and Sorensen, D, 1999, LAPACK Users’ Guide, (3rd Edition), SIAM, Philadelphia, https://www.netlib.org/lapack/lug

Golub, G H and Van Loan, C F, 1996, Matrix Computations, (3rd Edition), Johns Hopkins University Press, Baltimore